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Identifying Heavy Goods Vehicle Driving Styles in the United Kingdom

Figueredo, Grazziela P, Agrawal, Utkarsh, Mase, Jimiama MM, Mesgarpour, Mohammad, Wagner, Christian, Soria, Daniele, Garibaldi, Jonathan M, Siebers, Peer-Olaf, John, Robert I (2019) Identifying Heavy Goods Vehicle Driving Styles in the United Kingdom. IEEE Transactions on Intelligent Transportation Systems, 20 (9). pp. 3324-3336. ISSN 1524-9050. E-ISSN 1558-0016. (doi:10.1109/TITS.2018.2875343) (KAR id:77039)

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Although driving behavior has been largely studied amongst private motor vehicles drivers, the literature addressing heavy goods vehicle (HGV) drivers is scarce. Identifying the existing groups of driving stereotypes and their proportions enables researchers, companies, and policy makers to establish group-specific strategies to improve safety and economy. In addition, insight into driving styles can help predict drivers' reactions and therefore enable the modeling of interactions between vehicles and the possible obstacles encountered on a journey. Consequently, there are also contributions to the research and development of autonomous vehicles and smart roads. In this paper, our interest lies in investigating driving behavior within the HGV community in the United Kingdom (U.K.). We conduct analysis of a telematics dataset containing the incident information on 21 193 HGV drivers across the U.K. We are interested in answering two research questions: 1) What groups of behavior are we able to uncover? 2) How do these groups complement current findings in the literature? To answer these questions, we apply a two-stage data analysis methodology involving consensus clustering and ensemble classification to the dataset. Through the analysis, eight patterns of behavior are uncovered. It is also observed that although our findings have similarities to those from previous work on driving behavior, further knowledge is obtained, such as extra patterns and driving traits arising from vehicle and road characteristics.

Item Type: Article
DOI/Identification number: 10.1109/TITS.2018.2875343
Subjects: Q Science > QA Mathematics (inc Computing science)
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Daniel Soria
Date Deposited: 04 Oct 2019 09:04 UTC
Last Modified: 16 Feb 2021 14:07 UTC
Resource URI: (The current URI for this page, for reference purposes)
Soria, Daniele:
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